Charge Transfer in Classical Molecular Dynamics Simulations of Met-enkephalin: Improving Traditional Force Field with Data Driven Models

TL;DR

This paper introduces a machine learning-based data driven model to accurately predict charge transfer and polarization effects in molecular dynamics simulations of Met-enkephalin, enhancing traditional force fields.

Contribution

The study develops a data driven machine learning model that captures geometric-dependent charge transfer in peptides, improving classical force fields without iterative polarization calculations.

Findings

The model accurately predicts atomic charges across conformations.

It reduces computational complexity compared to ab initio methods.

Improves the realism of molecular dynamics simulations of peptides.

Abstract

The charge transfer and polarization effects are important components in the molecular mechanism description of bio-molecules. Classical force field with fixed point charge cannot take into the account of the non-negligible correlation between atomic charge and structure changes. In this work, high throughput ab initio calculations for the pentapeptide Met-enkephalin (MetEnk) reveal that geometric dependent charge transfer among residues is significant among tens of thousands of conformations. And we suggest a data driven model with machine learning algorithms to solve the geometric dependent charge fluctuations problem. This data driven model can directly provide ab initio level atomic charges of any structure for MetEnk, and avoids self-consistent iteration in polarizable force field. Molecular dynamics simulations demonstrated that the data driven model provides a possible choice to describe the explicit charge flux with minor modification of available classical force fields. This work provides us an alternative molecular mechanism model for future dynamics simulation of oligopeptides.